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Explore the Linear Regression Using Python
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Course Content

Explore the Linear Regression Using Python

Explore the Linear Regression Using Python

1. What is the Linear Regression?
Basic Understanding of the Linear RegressionHow Mathematically Does It Work?Build the Linear RegressionWorking with DatasetUseful Functions for Researching and Visualizing Dataset
2. Correlation
What Is Correlation? How To Find Pearson CoefficientCorrelation MatrixCalculating the Pearson Coefficient Using NumPy and PandasChallenge
3. Building and Training Model
Train-split evaluationFittingPredictionChallenge
4. Metrics to Evaluate the Model
ResidualsMean Absolute Error (MAE)MSE and RMSER-squaredNot Always the Linear Regression?
5. Multivariate Linear Regression
Building the Regression Model Based on Several VariablesTry to EvaluateChallenge

bookPrediction

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              
12

            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))
copy

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).

This value is the same as if we had substituted the line b + kx, where b is the estimated intersection with the model, and k is the slope. Please note that we multiply by 1 since the number of total phenols is 1 (x = 1).


              
1

            
prediction = model.intercept_ + model.coef_*1
copy

We can also put our testing data to get predictions for all amounts of flavanoids:


              
1

            
y_test_predicted = model.predict(X_test)
copy

Task

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

  1. [Line #6] Import the numpy library.
  2. [Line #26] Initialize the linear regression model.
  3. [Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
  4. [Line #31] Predict amount of flavanoids
  5. [Line #32] Print the predicted amount of flavanoids.

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Section 3. Chapter 3
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bookPrediction

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              
12

            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))
copy

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).

This value is the same as if we had substituted the line b + kx, where b is the estimated intersection with the model, and k is the slope. Please note that we multiply by 1 since the number of total phenols is 1 (x = 1).


              
1

            
prediction = model.intercept_ + model.coef_*1
copy

We can also put our testing data to get predictions for all amounts of flavanoids:


              
1

            
y_test_predicted = model.predict(X_test)
copy

Task

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

  1. [Line #6] Import the numpy library.
  2. [Line #26] Initialize the linear regression model.
  3. [Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
  4. [Line #31] Predict amount of flavanoids
  5. [Line #32] Print the predicted amount of flavanoids.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 3
toggle bottom row

bookPrediction

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              
12

            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))
copy

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).

This value is the same as if we had substituted the line b + kx, where b is the estimated intersection with the model, and k is the slope. Please note that we multiply by 1 since the number of total phenols is 1 (x = 1).


              
1

            
prediction = model.intercept_ + model.coef_*1
copy

We can also put our testing data to get predictions for all amounts of flavanoids:


              
1

            
y_test_predicted = model.predict(X_test)
copy

Task

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

  1. [Line #6] Import the numpy library.
  2. [Line #26] Initialize the linear regression model.
  3. [Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
  4. [Line #31] Predict amount of flavanoids
  5. [Line #32] Print the predicted amount of flavanoids.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              
12

            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))
copy

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).

This value is the same as if we had substituted the line b + kx, where b is the estimated intersection with the model, and k is the slope. Please note that we multiply by 1 since the number of total phenols is 1 (x = 1).


              
1

            
prediction = model.intercept_ + model.coef_*1
copy

We can also put our testing data to get predictions for all amounts of flavanoids:


              
1

            
y_test_predicted = model.predict(X_test)
copy

Task

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

  1. [Line #6] Import the numpy library.
  2. [Line #26] Initialize the linear regression model.
  3. [Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
  4. [Line #31] Predict amount of flavanoids
  5. [Line #32] Print the predicted amount of flavanoids.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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